Methods and Systems for Artificial Cognition

1. An artificial intelligence system comprising: at least one perceptual module configured to compress input received by the system to generate an internal representation that is an abstraction of features of the input received by the system; at least one cortical module configured to process the internal representation; an action selection controller configured to coordinate communication of the internal representation between the at least one perceptual module and the at least one cortical module.

2. The system of claim 1 , wherein the at least one perceptual module is further configured to decompress the internal representation.

3. The system of claim 1 , wherein the at least one perceptual module is composed of spiking artificial neurons.

4. The system of claim 1 , wherein the at least one perceptual module is composed of non-spiking artificial neurons.

5. The system of claim 1 , wherein the at least one cortical module is composed of spiking artificial neurons.

6. The system of claim 1 , wherein the at least one cortical module is composed of non-spiking artificial neurons.

7. The system of claim 1 , wherein the action selection controller is composed of spiking artificial neurons.

8. The system of claim 1 , wherein the action selection controller is composed of non-spiking artificial neurons.

9. The system of claim 1 , further comprising at least one motor module configured to decompress the transformed internal representation.

10. The system of claim 9 , wherein the at least one motor module is further configured to compress the internal representation.

11. The system of claim 9 , wherein the at least one motor module is composed of spiking artificial neurons.

12. The system of claim 9 , wherein the at least one motor module is composed of non-spiking artificial neurons.

13. The system of claim 1 , wherein the internal representation is a semantic pointer representation.

14. The system of claim 1 , wherein the at least one cortical module processes the internal representation using superposition and a compression operator, wherein the compression operator is selected from the group consisting of: circular convolution, element-wise multiplication, principle component extraction, and learned operators.